Field of the Disclosure
Aspects of the present disclosure relate to metabolomics analysis and, more particularly, to a method, system, and computer program product for analyzing metabolomics data for a plurality of metabolites in a sample.
Description of Related Art
Sophisticated software systems have been developed for processing and analyzing metabolomic datasets. One exemplary system may comprise, for example, core LIMS functionality (sample tracking, management), instrument integration, automated data processing, visualization/reporting tools, data quality/review tools, and statistical analysis functionality. One positive aspect of running studies of consistently high-quality in high-throughput, is that an enormous knowledgebase is formed over time. Metabolites in the library, both known and unknown, that are identified in the studies are associated, for example, with pathways, public id's, physical properties, sample metadata, matrix types, etc. and also contain statistical data in the context of the study. This means that for any particular metabolite, there may be many studies in which that metabolite, for example, was identified, involving multiple pathways, disease states or other associated metadata. This knowledge and accumulated information may be extremely valuable in biomarker discovery, mechanism identification, optimization or other questions pertaining to metabolite function. In this regard, software and hardware systems are readily scalable for sample processing capacity and readily refined for improving data quality.
However, there still exists a bottleneck with respect to this wealth of information, in terms of biochemical interpretation. That is, it may not necessarily be realistic to provide significant automation to the process of metabolite analysis result interpretation, but, lacking such automation, there are significantly limited mechanisms for leveraging this wealth of past knowledge.
There also exist relatively simple pathway associations for metabolites, limited, for example, to super-pathways (e.g., carbohydrate pathways) and sub-pathways (e.g., pyrimidine degradation pathways). However, complex hierarchical associations such as, for example, inter- and intra-pathway relationships, though desirable, may be lacking in the state-of-the-art. This may result, for example, in deficiencies in performing complex biochemical pathway analysis, such as enrichment analysis, and deficiencies in visualizing those identified relationships.
One other deficiency of current available systems is that, for example, since there is no easily accessible storage mechanism for relating metadata, statistics, and pathways, the wealth of metabolite data may not be easily shared and understood by collaborators.